System and method for conducting a textual data search

ABSTRACT

A system and a method for conducting a textual data search includes receiving a search query associated with a search topic; analyzing the search query to determine at least one attribute of the search topic; processing the at least one attribute and a plurality of articles in a database; and identifying one or more results being relevant to the search topic in the plurality of articles in the database.

TECHNICAL FIELD

The present invention relates to a system and method for conducting atextual data search, and particularly, although not exclusively, to asystem and method for conducting a literature search and identifyingcitation data.

BACKGROUND

Textual contents may be digitally contained in a document stored in anelectronic database. In general, when a user needs to retrieve thetextual data in a document stored in a large database, the user willneed to locate the specific document from multiple documents within theone or more databases.

Locating or searching specific documents or articles may involvematching a search query with the information stored within thedocuments. However, it may be difficult to locate some of the storeddocuments if the search query is not accurately formulated in someoccasions, which may cause the searching process to become timeconsuming and inefficient.

SUMMARY OF THE INVENTION

Embodiments of the present invention improve the accuracy of citationrecommendation systems and potentially other document retrieval systemsby adding a “wisdom of crowds” feature based on citation network andcontent similarity. To evaluate whether the query document should cite aspecific document or not, it is reasonable to gather the crowds'opinions on this matter, i.e., whether each of the remaining documentsin the corpus cites it or not.

The opinions or citing decisions of documents that are more similar tothe query document should be given more weight because semantically (ortopically) similar documents are more likely to share the same set ofreferences/citations. The usefulness and value of this “wisdom ofcrowds” feature is attributed to the fact that researchers or scholarsput a lot of thought into their decisions of identifying the mostrelevant references and citations for their work.

Various embodiments of the present invention concerninformation-retrieval systems, which may be used to providerecommendations based on topic modelling of textual data. The presentinvention further relates generally to information science and moreparticularly to the fields of bibliometrics and scientometrics.

In accordance with a first aspect of the present invention, there isprovided a method of conducting a textual data search, comprising thesteps of: receiving a search query associated with a search topic;analyzing the search query to determine at least one attribute of thesearch topic; processing the at least one attribute and a plurality ofarticles in a database; and identifying one or more results beingrelevant to the search topic in the plurality of articles in thedatabase.

In an embodiment of the first aspect, the at least one attributeincludes a topical similarity between the search query and each of theplurality of articles in the database.

In an embodiment of the first aspect, the method further comprises thestep of constructing the topical similarity based on text information ofboth the search query and each of the plurality of articles in thedatabase.

In an embodiment of the first aspect, the step of processing the atleast one attribute and the plurality of articles in the databasefurther includes inferring at least one relevant topic and a pluralityof topic distribution associated with the search query and the pluralityof articles in the database over the at least one relevant topic.

In an embodiment of the first aspect, the processing of the at least oneattribute and the plurality of articles in the database is based onLatent Dirichlet Allocation (LDA).

In an embodiment of the first aspect, the topical similarity between thesearch query and each of the plurality of articles in the database isrepresented as a cosine similarity of the plurality of topicdistribution.

In an embodiment of the first aspect, the cosine similarity isrepresented as:

${{topical\_ similarity}_{qd} = {{{Cosine}( {Q,D} )} = \frac{Q \cdot D}{{Q}{D}}}};$

wherein Q denotes a multinomial distribution of the search query q overthe at least one relevant topic and D denotes a multinomial distributionof an article d in the database over the at least one relevant topic.

In an embodiment of the first aspect, the at least one attributeincludes an aggregate likelihood that assesses whether each of theplurality of articles is to be cited by other articles with a similartopic in the database.

In an embodiment of the first aspect, the at least one attribute furtherincludes crowd-based information associated with a list of references ineach of the articles in the database.

In an embodiment of the first aspect, the aggregate likelihood isassociated with the topical similarity between each of the plurality ofarticles and the other articles in the database.

In an embodiment of the first aspect, the aggregate likelihood isrepresented as:

aggregate_likelihood_being_cited_(qd)=Σ_(i)^(n-1)topical_similarity_(ci) ·c _(id);

wherein i denotes an article in the database except for the article d inthe database, c_(id) denotes a binary variable which represents acitation relationship of the article i to the article d, andtopical_similarity_(qi) denotes the topical similarity between thesearch query and the article i.

In an embodiment of the first aspect, the aggregate likelihood may benormalized by the citation count of the article and represented as:

aggregate_likelihood_normalized_(qd)=Σ_(i) ^(n-1)topical_similarity_(qi)·c _(id)/Σ_(i) ^(n-1) c _(id).

In an embodiment of the first aspect, the method further comprises thestep of representing c_(id) with a citation matrix containing aplurality binary variables each represent a citation relationship of thearticle i to the article d.

In an embodiment of the first aspect, the method further comprises thestep of determining a score for each of the plurality of articles in thedatabase, wherein the score is related to a linear representation of theat least one attribute of the search topic.

In an embodiment of the first aspect, the linear representation includesa weighted sum of the at least one attribute.

In an embodiment of the first aspect, the weighted sum of the at leastone attribute is represented as: score(q,d)=Σ_(i)w_(i)×f_(i)(q,d);wherein q denotes the search query, d denotes an article in thedatabase, and w_(i) denotes a feature weight assigned for each of the atleast one attribute f_(i)(q,d).

In an embodiment of the first aspect, the feature weight is determinedbased on a linear classifier.

In an embodiment of the first aspect, the linear classifier includes atleast one of a logistic regression method and a Support Vector machinefor optimizing Mean Average Precision method.

In an embodiment of the first aspect, the method further comprises thestep of obtaining the one or more result representing the one or more ofthe plurality of articles in the database in an order according to thedetermined score.

In accordance with a second aspect of the present invention, there isprovided a system for use in conducting a textual data search,comprising: a search input module arranged to receive a search queryassociated with a search topic and to analyze the search query todetermine at least one attribute of the search topic; and a databaseprocessing module arranged to process the at least one attribute and aplurality of articles in a database, and to identify one or more resultsbeing relevant to the search topic in the plurality of articles in thedatabase.

In an embodiment of the second aspect, the at least one attributeincludes a topical similarity between the search query and each of theplurality of articles in the database.

In an embodiment of the second aspect, the search input module isfurther arranged to construct the topical similarity based on textinformation of both the search query and each of the plurality ofarticles in the database.

In an embodiment of the second aspect, the at least one attributeincludes an aggregate likelihood in which each of the plurality ofarticles is to be cited by other articles with a similar topic in thedatabase.

In an embodiment of the second aspect, the at least one attributefurther includes crowd-based information associated with a list ofreferences in each of the articles in the database.

In an embodiment of the second aspect, the aggregate likelihood isassociated with the topical similarity between each of the plurality ofarticles and the other articles in the database.

In an embodiment of the second aspect, the database processing module isfurther arranged to determine a score for each of the plurality ofarticles in the database, wherein the score is related to a linearrepresentation of the at least one attribute of the search topic.

In an embodiment of the second aspect, the one or more result representsone or more of the plurality of articles in the database in an orderaccording to the determined score.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention will now be described, by way ofexample, with reference to the accompanying drawings in which:

FIG. 1 is a schematic diagram of a computing server for operation as asystem for use in conducting a textual data search in accordance withone embodiment of the present invention;

FIG. 2 is a schematic diagram of an embodiment of the system for use inconducting a textual data search in accordance with one embodiment ofthe present invention;

FIG. 3 is a flow diagram showing an example process of the method ofconducting a textual data search in accordance with one embodiment ofthe present invention; and

FIG. 4 is a diagram showing an example citation network representing acitation relationship between a search query and a plurality of articlesin a database.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The inventors have, through their own research, trials and experiments,devised that literature search is a tedious and time-consuming work forresearchers, and it is often difficult to find a complete list ofrelevant articles.

One of the difficulties in literature search is the composition of anappropriate query for a literature search engine. For example,text-based search engines often give poor results when there is avocabulary mismatch between the query and relevant documents. To addressthis problem, in one example embodiment, a different method may be used,in which the search engine takes a research project description such asan abstract as the query input and recommends a list of possiblecitations as the output.

Preferably, compared with the keyword-based query, the abstract-basedquery not only contains richer information, but also relieves users fromthe burden of identifying the most appropriate query words.Advantageously, the longer query does not necessarily have to be awell-written abstract. Any related keywords can be simply added to thequery input, since the citation recommendation method is based on thebag-of-words assumption, that is, the sequence of words in a sentence isneglected.

Without wishing to be bound by theory, authors' citation choices may beinfluenced by various factors. The search method may involve a number offeatures such as content similarity, author characteristics, articlevenue characteristics, and authors' citation behavior. To implementthese features, much information needs to be collected from differentsources. The cost of such data collection efforts can be substantial inpractice and thus may prevent the wide adoption of some features.

In one preferred embodiment, there is provided a lightweight citationrecommendation method based on readily available information includingarticle abstracts and citation networks (which may be constructed fromarticles' reference lists). By drawing from the topic modelingliterature, a new feature such as aggregate likelihood of being cited bysimilar articles to exploit the “wisdom of crowds” may be embedded inthe citation networks of academic articles.

In some examples, many of the identified features are more or lessrelated with article citation networks. For instance, the citation countof an article is its in-degree in the citation network. For example, ifa journal has a high impact factor, the articles published in thatjournal may be cited by many other articles. Self-citation essentiallyinvolves a paper citing another paper, which has one or more authors incommon. Articles on related topics may cite the same list of seminalworks, which may imply that a new article on these topics should alsocite the seminal works as inferred from the citation network.

Instead of adding many features that are intended to capture differentthings, the inventors devised that one simple “wisdom of crowds” measuremay potentially achieve the same purpose but at a much lower cost.Preferably, the method in accordance with the embodiments of the presentinvention only involves two features, however the method may achieve asimilar level of accuracy according to experiments on a standard dataset(i.e., ACL Anthology Reference Corpus) compared with other examplemethods which may use many additional features.

With reference to FIG. 1, an embodiment of the present invention isillustrated. This embodiment is arranged to provide a system for use inconducting a textual data search, comprising: a search input modulearranged to receive a search query associated with a search topic and toanalyze the search query to determine at least one attribute of thesearch topic; and a database processing module arranged to process theat least one attribute and a plurality of articles in a database, and toidentify one or more results being relevant to the search topic in theplurality of articles in the database.

Preferably, in one example, the system may be used as an informationretrieval system which may output one or more results of a textualcontent relevant to a search topic. By using the search method inaccordance with the embodiments of the present invention, the resultsmay be provided as a list of articles in an order based on theattributes/features of the search query and the articles in the databaseand the scores of each of the identified articles in the list.

In this embodiment, the search input module and the database processingmodule are implemented by or for operation on a computer having anappropriate user interface. The computer may be implemented by anycomputing architecture, including stand-alone PC, client/serverarchitecture, “dumb” terminal/mainframe architecture, or any otherappropriate architecture. The computing device is appropriatelyprogrammed to implement the invention.

Referring to FIG. 1, there is shown a schematic diagram of a computer ora computing server 100 which in this embodiment comprises a server 100arranged to operate, at least in part if not entirely, the system foruse in conducting a textual data search in accordance with oneembodiment of the invention. The server 100 comprises suitablecomponents necessary to receive, store and execute appropriate computerinstructions. The components may include a processing unit 102,read-only memory (ROM) 104, random access memory (RAM) 106, andinput/output devices such as disk drives 108, input devices 110 such asan Ethernet port, a USB port, etc. Display 112 such as a liquid crystaldisplay, a light emitting display or any other suitable display andcommunications links 114. The server 100 includes instructions that maybe included in ROM 104, RAM 106 or disk drives 108 and may be executedby the processing unit 102. There may be provided a plurality ofcommunication links 114 which may variously connect to one or morecomputing devices such as a server, personal computers, terminals,wireless or handheld computing devices. At least one of a plurality ofcommunications link may be connected to an external computing networkthrough a telephone line or other type of communications link.

The server may include storage devices such as a disk drive 108 whichmay encompass solid state drives, hard disk drives, optical drives ormagnetic tape drives. The server 100 may use a single disk drive ormultiple disk drives. The server 100 may also have a suitable operatingsystem 116 which resides on the disk drive or in the ROM of the server100.

The system has a database 120 residing on a disk or other storagedevice, which is arranged to store at least one record 122. The database120 is in communication with the server 100 with an interface, which isimplemented by computer software residing on the server 100.

Alternatively, the database 120 may also be implemented as a stand-alonedatabase system in communication with the server 100 via an externalcomputing network, or other types of communication links.

In one preferred embodiment, the server 100 may be used as a citationrecommendation system for identifying relevant citations for a researchstudy.

For example, a topic models-based method may be used to predict thepresence of a citation link between every pair of documents based on thetext content of the documents. It may utilize the citation context(i.e., the words around citation placeholders, or citing snippets) inaddition to the text content to recommend a list of reference papersgiven the input of a manuscript.

In an alternative example, a citation recommendation framework may useboth content and citation graph-based information. Various measures areconstructed from the citation graph including co-citation coupling, sameauthor, Katz, and citation count. Katz, which refers to the number ofunique paths between two articles exponentially damped by length,represents the connection closeness between two documents. The recencyof publication is also included in the feature set. Then, the featuresare combined in a linear model to produce a document score, which may beused to rank the documents.

In addition, a feature set may be enriched with topical similarity andauthor behavioral patterns including scientific article popularity,recency, citing snippets, topic-related citing pattern, and socialhabits. In these examples, two different linear classifiers, namelylogistic regression model and SVM-MAP, may be utilized to learn theweights of all the features in the retrieval model. Besides, aniterative paradigm may be applied for learning model weights. The methodis found to be outperforming the single-iteration training.

Preferably, the method may incorporate one or more attributes orfeatures when constructing the relevance score. These attributes relateto information such as article metadata, author metadata, publicationvenue (e.g., journal, conference, workshop) metadata, and authors'citing behavioural patterns or habits. For instance, citation count,year of publication, co-citation, and self-citation can be incorporatedin the calculation of the relevance score.

To calculate a relevance score based on multiple features, a linear ornonlinear model may be adopted. For example, a simple linear model mayscore each document against the query as a weighted sum of variousfeature scores. The feature weights can be obtained by fitting alogistic regression or the Support Vector machine for optimizing MeanAverage Precision (SVM-MAP) model.

A relevance score system may be applied to score or to sort thedocuments in a large corpus or database of documents with respect to aquery (e.g., short description, abstract, full article, search terms)and then rank documents to identify candidates to be recommended aspotential citations.

For example, the relevance score system may involve the ranking ofrelevance scores based on content similarity. Content similarity may becalculated based on a TF-IDF (Term Frequency-Inverse Document Frequency)score.

The scoring system may be alternatively based on topic modeling, whichmay determine the content similarity score, specifically, topicalsimilarity, etc. between the query and a document in the corpus using avector space model along with cosine similarity. Further discussionswill be included in the later parts of the disclosure.

Based on the abovementioned features, the inventors devise thataggregate likelihood of being cited by similar articles which capturesthe wisdom of crowds in the reference lists of academic articles may beused. In addition, the topical similarity feature which measures contentsimilarity based on topic models may also be used in some exampleembodiments.

In accordance with one aspect the present invention, the improved systemand method provided in accordance with the following embodimentsoutperform existing citation recommendation systems and methods whichmay require many features in order to guarantee the accuracy of therecommendation systems. In practice, many of these features may berelated to the citation network formed by documents in the large corpus.For example, the citation count of a document may be its in-degree inthe citation network.

Without wishing to be bound by theory, if a journal has a high impactfactor, the documents published in that journal may be on average citedby many other documents. Self-citation essentially involves a documentciting another document, which has one or more authors in common.Documents on related topics typically cite the same list of seminalworks, which implies that a new document on these topics should alsocite the seminal works as inferred from the citation network.

With reference to FIG. 2, there is shown an embodiment of the system 200for use in conducting a textual data search. In this embodiment, theserver 100 is used as part of a search engine 200 arranged to conduct asearch of the articles stored in the database 210. In this embodiment,the search engine 200 may communicate with a database 210 which may beexternal to the search engine 200 including the search input module 202and the database processing module 204.

Preferably, the system 200 is arranged to receive a search query 206 andreturn one or more articles in a database which is relevant to thetopic(s) as a list of results 208. In an example searching process, thesearch input module 202 may receive and process the search query 206 toderive the necessary features or attributes relevant to a search topic.These attributes may be passed to the database processing module 204 forfurther processing. For example, the database processing module 204 mayaccess one or more databases 210 according to the search requirement andprocess the attributes to identify the relevance of each of the articlesin the database 210 to the search topic or search query 206.

Alternatively, the database 210 may be locally included in the samesystem 200 for use in conducting a textual data search, or search engine200 including the search input module 202 and the database processingmodule 204 may be selectively implemented in a database 210 forfacilitating a search of the articles stored in the database 210.

As discussed above, the search query 206 may be in a form of a briefdescription or an abstract of the topic and may not necessarily be inform a keyword limited search with Boolean operators as appreciated by askilled person in the art.

For example, a user may input a search query 206 including a searchtopic to the search system 200, the search input module 202 may thenanalyse the search query 206 and identify one or morefeatures/attributes of the input search topic. As discussed earlier,these attributes may include at least a topical similarity between thesearch query and each of the plurality of articles in the database, aswell as an aggregate likelihood in which the search articles may becited by other articles with a similar topic in the database.

In one example search process, the search input module 202 firstidentifies two features of great importance to authors' citationchoices. Then the database processing module 204 may process theidentified attributes and the articles in the database 210. The databaseprocessing module 204 may further score each document d_(i) against thequery (i.e., an abstract) q as a weighted sum of features scores.Preferably, scores may be assigned to each of the articles in thedatabase 210 for a single search query q as follows:

$\begin{matrix}{{{score}( {q,d} )} = {\sum\limits_{i}{w_{i} \times {f_{i}( {q,d} )}}}} & (1)\end{matrix}$

In this example, the score is related to a linear representation of theattributes of the search topic.

Two features/attributes, namely topical similarity and aggregatelikelihood of being cited by similar articles, are developed fromdifferent perspectives. Preferably, topical similarity may beconstructed based on text information of both the search query and eachof the plurality of articles in the database, which is helpful infinding similar or topically-related articles.

Besides, aggregate likelihood of being cited by similar articles may beconstructed from authors' perspective to capture the wisdom of crowds incitation choices. For example, this attribute may include crowd-based orcrowd-sourced information associated with a list of references in eachof the articles in the database. This feature helps to find not onlyrelated works, but also their citation choices.

Preferably, topical similarity may represent the topical relationshipsbetween two documents in a more explicit way than the content/textsimilarity, which may be considered as an important feature to identifythe document relevance. In one example, the process includes inferringat least one relevant topic and a plurality of topic distributionassociated with the search query and the plurality of articles in thedatabase over the at least one relevant topic, which may be preferablybased on Latent Dirichlet Allocation (LDA).

The basic idea of topic modelling algorithms such as Latent DirichletAllocation (LDA) is that documents may be generated by choosing adistribution over a set of latent topics and that each topic is in turncharacterized by a distribution over words. Topic models may also assumethat each document may contain multiple topics. For example, eachdocument may be characterized by a distribution over a set of topics(i.e., topical distribution), which may be represented by a vector.

With the topical distributions of the search query over the relevanttopics and the topical distribution of each of the articles in thedatabase over the relevant topics, the topical similarity between thesearch query and each of the plurality of articles in the database isrepresented as a cosine similarity of the plurality of topicdistribution. Preferably, the cosine similarity may be represented asfollows:

$\begin{matrix}{{topical\_ similarity}_{qd} = {{{Cosine}( {Q,D} )} = \frac{Q \cdot D}{{Q}{D}}}} & (2)\end{matrix}$

wherein Q denotes a multinomial distribution of the search query (i.e.,an abstract) q over the relevant topics and D denotes a multinomialdistribution of an article d in the database/corpus over the relevanttopics.

As discussed above, the attribute of aggregate likelihood of being citedby similar articles captures the wisdom of crowds in the reference listsof academic articles. The method aggregates the citation decisions ofall other articles regarding one candidate article, and may put moreweights to the citation choices of topically similar papers. In themeantime, it may also capture the general consensus in citation choices(e.g., scientific article popularity). The underlying rationale is thatprior citations choices by similar articles provide value for authorswhen deciding which papers to cite in their studies.

In this example, the aggregate likelihood is associated with the topicalsimilarity between each of the plurality of articles and the otherarticles in the database. In addition, an article citation network whichrepresents a citation relationship between the search query and theplurality of articles in the database may be used in the construction ofthe feature of aggregate likelihood. With reference to FIG. 4, there isshown an example citation network associated with a search query andfour articles d₁ to d₄ in a literature database.

Preferably, the aggregate likelihood may be represented as:

aggregate_likelihood_being_cited_(qd)=Σ_(i)^(n-1)topical_similarity_(ci) ·c _(id)  (3)

wherein i denotes an article in the database except for the article d inthe database, c_(id) denotes a binary variable which represents acitation relationship of the article i to the article d, andtopical_similarity_(qi) denotes the topical similarity between thesearch query and the article i. Alternatively or optionally, c_(id) maybe represented as a citation matrix containing a plurality binaryvariables each represent the citation relationship of the article i tothe article d.

With the two features being determined and processed by the search inputmodule and the database processing module, the database processingmodule may further determine a score for each of the plurality ofarticles in the database, and may obtain the list of results 208representing the one or more of the plurality of articles in thedatabase in an order according to the determined score.

For example, the score may be related to a linear representation of theat least one attribute of the search topic. In this linearrepresentation, the ranking score or the linear representation may becalculated as a weighted sum as follows.

score(q,d)=w ₁×topical_similarity_(qd) +w₂×aggregate_likelihood_being_cited_(qd)  (4)

where w₁ and w₂ are the feature weights. The two features may be putinto log-space to better fit into the model, the improved performanceachieved was illustrated in experimental results on the development setafter log-transformation.

Alternatively, the weighted sum of the at least one attribute may berepresented as:

score(q,d)=Σ_(i) w _(i) ×f _(i)(q,d)  (1)

wherein q denotes the search query, d denotes an article in thedatabase, and w_(i) denotes a feature weight assigned for each of the atleast one attribute f_(i)(q,d).

The feature weight may be determined based on a linear classifier. Inone example, the top N articles for each search query (i.e., anabstract) may be collected based on the single feature of aggregatelikelihood of being cited by similar articles in a descending order. Theretrieved articles may be labelled with +1 if they appear in thereference lists of the query article, or −1 otherwise. Then, a linearclassifier may be used to learn the weights for the two features on thistraining data.

Preferably, the linear classifier may include a logistic regressionmethod or a Support Vector machine for optimizing Mean Average Precision(SVM-MAP) method. Logistic regression may measure the relationshipbetween document relevance (i.e., a binary indicator) and the twofeatures by estimating probabilities using a logistic function, andSVM-MAP is a learning technique which may train a support vector machineto directly optimize mean average precision. The inventors confirmedthat consistent results may be obtained using any one of these twolinear classifiers.

In one illustrative example, it is assumed that only four documents (d₁,d₂, d₃, d₄) are present in the corpus/database and a research abstractis entered as a query. The trained LDA model is first loaded to inferthe topic distribution of the query text. Then the first feature of thetopical similarity between the query text and every document in thecorpus is determined based on equation (2).

Suppose that the topical similarity between the query text and d₁, d₂,d₃, and d₄ is 0.5, 0.1, 0.1, and 0.3, respectively:

topical_similarity_(q)=[0.5 0.1 0.1 0.3]  (5),

where each element in the vector a_(i) represents the topical similaritybetween the query text and d_(i). The citation relationships can berepresented by the citation matrix

$\begin{matrix}{{{citation\_ matrix} = \begin{bmatrix}0 & 1 & 1 & 0 \\0 & 0 & 1 & 0 \\0 & 0 & 0 & 0 \\0 & 0 & 1 & 0\end{bmatrix}},} & (6)\end{matrix}$

where each element in the matrix b_(ij) (i.e., the value in row i andcolumn j) denotes the existing citation relationship between d_(i) andd_(j).

In this example, if d_(i) cites d_(j), b_(ij)=1, otherwise, b_(ij)=0.

Referring to FIG. 4, there is shown an example citation networkrepresenting the citing relationship and the topical similaritiesbetween the search query and the articles d_(i), where i=1, 2, 3 and 4.To calculate the second feature of the aggregate likelihood of beingcited for each document in the corpus, a topical citation network isfirst constructed based on the topical similarity and citation matrix asshown in equations (5) and (6). In the network, the solid lines witharrows represent the existing citation relationships among the documentsin the corpus. Specifically, each directed line denotes a documentciting another document. For example, d₁ cites both d₂ and d₃. Thedotted lines represent the topical similarity between the query text anddocuments in the corpus.

The aggregate likelihood of being cited feature for each document isconstructed as the summation of whether every other document cites thisdocument weighted by the topical similarity between the query text andevery other document. For instance, this feature for d₃ is0.5×1+0.1×1+0.3×1=0.9. Similarly, the feature for d₂, d₄ is 0, 0.5, and0, respectively. The calculation of the aggregate likelihood of beingcited may also be represented by a matrix multiplication as follows:

$\begin{matrix}{{{aggregate\_ likelihood}{\_ being}{\_ cited}_{q}} = {{{topical\_ similarity}_{q} + {citation\_ matrix}} = \lbrack {0\mspace{14mu} 0.5\mspace{14mu} 0.9\mspace{14mu} 0} \rbrack}} & (7)\end{matrix}$

where each element c_(i) represents the feature of the aggregatelikelihood being cited for d_(i).

Let the weights of topical similarity and aggregate likelihood of beingcited equal to 0.5 in the linear model as shown in equation (4). Thus,the ranking score between the query text and d_(i) equals to0.5×α_(i)+0.5×c_(i). For example, the ranking score for d₁ is0.5×0.5+0.5×0=0.25.

The ranking scores of each of the articles relevant to the search querymay be represented as follows:

$\begin{matrix}{{ranking\_ scores}_{q} = {{{0.5 \times {topical\_ similarity}_{q}} + {0.5 \times {aggregate\_ likelihood}{\_ being}{\_ cited}_{q}}} = {\quad\lbrack {0.25\mspace{14mu} 0.3\mspace{14mu} 0.5\mspace{14mu} 0.15} \rbrack}}} & (8)\end{matrix}$

where each element in the vector represents the ranking score of d_(i)given the query text.

According to the ranking scores, the four documents will be recommendedin the order of d₃, d₂, d₁, d₄.

With reference to FIG. 3, there is shown a flow chart summarizing theentire process of the method for conducting a search as described above.

These embodiments may be advantageous in that the search method may beused in analyzing search queries in a form of a brief description suchas a research abstract instead of the form of keywords in traditionalsearch engines. Therefore, the results will be less likely to omit anyresults due to typographic errors or imprecise keyword matching.

Advantageously, the method does not require users to formulate theirsearch queries based on keywords, and therefore it is not necessary forthe users to try multiple queries using different keywords. In contrast,search queries consisting of a brief project description such as anabstract may be used as the query input accordance to the embodiments ofthe present invention.

The abstract-based query not only contains richer information but alsorelieves users from the burden of identifying the most appropriatekeywords. The query does not necessarily need to be a well-writtenabstract. A combination of keywords would be sufficient as the queryinput, as the method is based on the bag-of-words assumption and whichmay also ignore the sequence of words in a sentence.

Advantageously, the recommended set of citations is scored and ranked bytopical similarity and aggregate likelihood of being cited by articlessimilar to the abstract-based query. The searching process can onlyinvolve two attributes/features and does not require other attributessuch as authors, co-authors, article venues, and citing snippets, etc.It may be difficult and time-consuming to collect all these kinds ofinformation in practice.

In addition, the method may also be used in applications forautomatically locating and recommending citations based on the textcontents (such as one or more paragraphs in an abstract field). Forexample, a word processor may be implemented to automatically locate andinsert a list of references which may be relevant to an article.

The inventors have evaluated the performances of the embodimentsaccording to the present invention. In the experiments, the citationrecommendation method according to one example embodiment isimplemented, and has been evaluated on a standard dataset (i.e., ACLAnthology Reference Corpus) compared with existing methods. It isobserved that the present invention with only two features involvedachieves a similar level of accuracy as existing methods which mayinvolve more features as abovementioned.

To evaluate the performance of the citation recommendation method, themethod is tested on a standard dataset, the ACL Anthology ReferenceCorpus (ACL-ARC), and is compared with other example methods. In thisexperiment, the dataset contains 10,921 articles and 38,767 referencesto articles inside the ACL-ARC from 1965 to 2007 in the field ofComputational Linguistics. The baseline search methods incorporate manyfeatures such as topical similarity and author behavioral patterns. Itis shown that the present method significantly outperforms both a textsimilarity baseline and other related models using similar features.

In the experiment, articles that do not satisfy the following conditionswere excluded. The included articles have full text with a documentlength exceeding 5 words; and have at least five references remainingafter discarding the references to articles outside the processedcorpus. Besides, articles published from 2000 to 2003 were used as atraining set, articles published in 2004 as development set, andarticles published from 2005 to 2006 as test set.

The summary statistics of the training and test set is shown in Table 1.Note that the features of aggregate likelihood of being cited by similararticles for all training and evaluation are constructed using thecitation information over previous years.

Train Dev Test Years 2000-2003 2004 2005-2006 Articles 619 318 864References 4,734 2,545 7,637 Refs/Article 7.6 8 8.8

The title and abstract of an article was used as the search query.Because abstracts are not annotated in the corpus, each article text wastruncated to the first 200 words as the query input. The system returnsa list of articles, which are candidates to be cited by the queryarticle. Then, the recommended list is compared against the referencelist of the query article using mean average precision as thequantitative measure.

Mean average precision (MAP) may be applied to evaluate a ranked listacross different queries in information retrieval (IR) systems. Thismeasure is sensitive to the rank of all relevant documents, and givesthe highest score when all correct predictions precede all incorrectpredictions. MAP is defined as the arithmetic mean of average precisionover a set of queries as shown in equation (10). Average precision iscalculated for each query as the average of precision at every cutoffwhere a new relevant document is retrieved as:

${AveP} = \frac{\sum\limits_{k = 1}^{m}{Precision}_{k}}{m}$

(9), where k denotes a point where a new relevant document is retrieved;m is the total number of relevant documents for a query.

$\begin{matrix}{{{MAP} = \frac{\sum\limits_{q \in Q}{AveP}_{q}}{Q}},} & (10)\end{matrix}$

where q is a query in a set of queries Q; and |Q| is the number ofqueries in the set.

The following Table shows the comparisons of mean average precision ofthe method in accordance with the embodiments of the present inventionwith the baseline method on the development set. N is the number ofarticles collected for each abstract to train the model. The MAP of thepresent invention increases over the baseline method by 15.44 whenN=100, and by 11.3 when N=2000, using the logistic model as the linearclassifier in learning. The mean average precisions of the presentinvention are consistent across the four groups, that is, groups withdifferent values of N or using different classifiers. When using SVM-MAPas the linear classifier, the MAP of the present method is only 3 pointssmaller than that of the baseline method. Considering that the baselinemethod incorporates 19 various features, the comparable mean averageprecision of the present method using only 2 features is moreadvantageous.

Dev N = 100 N = 2000 Logistic (Wisdom of Crowds) 23.3 22.0 Logistic(Baseline) 7.9 10.7 SVM-MAP (Wisdom of Crowds) 22.6 22.5 SVM-MAP(Baseline) 25.3 25.5

To evaluate the effect of each feature on authors' citation choices, theinventors have conducted a feature analysis by comparing the meanaverage precision of the models using only topical similarity oraggregate likelihood of being cited and the model using both features.As shown in Table 3 below, in which the feature weights are trainedusing logistic regression on the training data (N=2000). Both topicalsimilarity and aggregate likelihood of being cited can produce a MAP ofmore than 13 on its own, which suggests that the two features areequally important in predicting authors' citation choices. Besides, thetwo features combined can produce an increase of nearly 9 points in MAP.It indicates that the two features provide complementary and equallyvaluable information for citation prediction.

Logistic (Wisdom of Crowds) Dev MAP Topical similarity 13.2 Aggregatelikelihood of being 13.6 cited by similar articles Both features 22.0

These embodiments are advantageous in providing a lightweight citationrecommendation method based on the wisdom of crowds in citation choices.The method involves only two features (i.e., topical similarity andaggregate likelihood of being cited by similar articles) may deliver asimilar/better performance as existing methods using many features. Themethod is highly efficient and relies on readily available informationincluding article abstracts and reference lists, and the method issuitable for large-scale implementation.

Although not required, the embodiments described with reference to theFigures can be implemented as an application programming interface (API)or as a series of libraries for use by a developer or can be includedwithin another software application, such as a terminal or personalcomputer operating system or a portable computing device operatingsystem. Generally, as program modules include routines, programs,objects, components and data files assisting in the performance ofparticular functions, the skilled person will understand that thefunctionality of the software application may be distributed across anumber of routines, objects or components to achieve the samefunctionality desired herein.

It will also be appreciated that where the methods and systems of thepresent invention are either wholly implemented by computing system orpartly implemented by computing systems then any appropriate computingsystem architecture may be utilised. This will include standalonecomputers, network computers and dedicated hardware devices. Where theterms “computing system” and “computing device” are used, these termsare intended to cover any appropriate arrangement of computer hardwarecapable of implementing the function described.

It will be appreciated by persons skilled in the art that the term“database” may include any form of organized or unorganized data storagedevices implemented in either software, hardware or a combination ofboth which are able to implement the function described.

It will be appreciated by persons skilled in the art that numerousvariations and/or modifications may be made to the invention as shown inthe specific embodiments without departing from the spirit or scope ofthe invention as broadly described. The present embodiments are,therefore, to be considered in all respects as illustrative and notrestrictive.

Any reference to prior art contained herein is not to be taken as anadmission that the information is common general knowledge, unlessotherwise indicated.

1. A method of conducting a textual data search, comprising the stepsof: receiving a search query associated with a search topic; analyzingthe search query to determine at least one attribute of the searchtopic; processing the at least one attribute and a plurality of articlesin a database; and identifying one or more results being relevant to thesearch topic in the plurality of articles in the database.
 2. A methodof conducting a textual data search in accordance with claim 1, whereinthe at least one attribute includes a topical similarity between thesearch query and each of the plurality of articles in the database.
 3. Amethod of conducting a textual data search in accordance with claim 2,further comprising the step of constructing the topical similarity basedon text information of both the search query and each of the pluralityof articles in the database.
 4. A method of conducting a textual datasearch in accordance with claim 2, wherein the step of processing the atleast one attribute and the plurality of articles in the databasefurther includes inferring at least one relevant topic and a pluralityof topic distribution associated with the search query and the pluralityof articles in the database over the at least one relevant topic.
 5. Amethod of conducting a textual data search in accordance with claim 4,wherein the processing of the at least one attribute and the pluralityof articles in the database is based on Latent Dirichlet Allocation. 6.A method of conducting a textual data search in accordance with claim 4,wherein the topical similarity between the search query and each of theplurality of articles in the database is represented as a cosinesimilarity of the plurality of topic distribution.
 7. A method ofconducting a textual data search in accordance with claim 6, wherein thecosine similarity is represented as:${{topical\_ similarity}_{qd} = {{{Cosine}( {Q,D} )} = \frac{Q \cdot D}{{Q{}D}}}};$wherein Q denotes a multinomial distribution of the search query q overthe at least one relevant topic and D denotes a multinomial distributionof an article d in the database over the at least one relevant topic. 8.A method of conducting a textual data search in accordance with claim 2,wherein the at least one attribute includes an aggregate likelihood inwhich each of the plurality of articles is to be cited by other articleswith a similar topic in the database.
 9. A method of conducting atextual data search in accordance with claim 8, wherein the at least oneattribute further includes crowd-based information associated with alist of references in each of the articles in the database.
 10. A methodof conducting a textual data search in accordance with claim 8, whereinthe aggregate likelihood is associated with the topical similaritybetween each of the plurality of articles and the other articles in thedatabase.
 11. A method of conducting a textual data search in accordancewith claim 10, wherein the aggregate likelihood is represented as:aggregate_likelihood_being_cited_(qd)=Σ_(i)^(n-1)topical_similarity_(qi) ·c _(id); wherein i denotes an article inthe database except for the article d in the database, c_(id) denotes abinary variable which represents a citation relationship of the articlei to the article d, and topical_similarity_(qi) denotes the topicalsimilarity between the search query and the article i.
 12. A method ofconducting a textual data search in accordance with claim 11, furthercomprising the step of representing c_(id) with a citation matrixcontaining the plurality binary variables each represent the citationrelationship of the article i to the article d.
 13. A method ofconducting a textual data search in accordance with claim 1, furthercomprising the step of determining a score for each of the plurality ofarticles in the database, wherein the score is related to a linearrepresentation of the at least one attribute of the search topic.
 14. Amethod of conducting a textual data search in accordance with claim 13,wherein the linear representation includes a weighted sum of the atleast one attribute.
 15. A method of conducting a textual data search inaccordance with claim 14, wherein the weighted sum of the at least oneattribute is represented as: score(q,d)=Σ_(i)w_(i)×f_(i)(q,d); wherein qdenotes the search query, d denotes an article in the database, andw_(i) denotes a feature weight assigned for each of the at least oneattribute f_(i)(q,d).
 16. A method of conducting a textual data searchin accordance with claim 15, wherein the feature weight is determinedbased on a linear classifier.
 17. A method of conducting a textual datasearch in accordance with claim 16, wherein the linear classifierincludes at least one of a logistic regression method and a SupportVector machine for optimizing Mean Average Precision method.
 18. Amethod of conducting a textual data search in accordance with claim 13,further comprising the step of obtaining the one or more resultrepresenting the one or more of the plurality of articles in thedatabase in an order according to the determined score.
 19. A system foruse in conducting a textual data search, comprising: a search inputmodule arranged to receive a search query associated with a search topicand to analyze the search query to determine at least one attribute ofthe search topic; and a database processing module arranged to processthe at least one attribute and a plurality of articles in a database,and to identify one or more results being relevant to the search topicin the plurality of articles in the database.
 20. A system for use inconducting a textual data search in accordance with claim 19, whereinthe at least one attribute includes a topical similarity between thesearch query and each of the plurality of articles in the database. 21.A system for use in conducting a textual data search in accordance withclaim 20, wherein the search input module is further arranged toconstruct the topical similarity based on text information of both thesearch query and each of the plurality of articles in the database. 22.A system for use in conducting a textual data search in accordance withclaim 20, wherein the at least one attribute includes an aggregatelikelihood in which each of the plurality of articles is to be cited byother articles with a similar topic in the database.
 23. A system foruse in conducting a textual data search in accordance with claim 22,wherein the at least one attribute further includes crowd-basedinformation associated with a list of references in each of the articlesin the database.
 24. A system for use in conducting a textual datasearch in accordance with claim 22, wherein the aggregate likelihood isassociated with the topical similarity between each of the plurality ofarticles and the other articles in the database.
 25. A system for use inconducting a textual data search in accordance with claim 19, whereinthe database processing module is further arranged to determine a scorefor each of the plurality of articles in the database, wherein the scoreis related to a linear representation of the at least one attribute ofthe search topic.
 26. A system for use in conducting a textual datasearch in accordance with claim 25, wherein the one or more resultrepresents one or more of the plurality of articles in the database inan order according to the determined score.